
# Importing the Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelBinarizer
import cv2
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score
import os
import tensorflow.keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
import seaborn as sns
import matplotlib.pyplot as plt


train_set = pd.read_csv("C:/Users/Sushant/Downloads/archive/sign_mnist_train.csv")
test_set = pd.read_csv("C:/Users/Sushant/Downloads/archive/sign_mnist_test.csv")


# Inspect our Training data
train_set.head()


# Description of our training set
train_set.describe()


# Checking the overall information of training set 
train_set.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27455 entries, 0 to 27454
Columns: 785 entries, label to pixel784
dtypes: int64(785)
memory usage: 164.4 MB


# Checking the distribution of labels
label_values = train_set['label'].values
plt.figure(figsize = (18,8))
sns.countplot(x =label_values)

<AxesSubplot:ylabel='count'>


training_array = np.array(train_set, dtype = 'float32')
testing_array = np.array(test_set, dtype = 'float32')


X_train = training_array[:, 1:] /255
X_test = testing_array[:, 1:] /255

X_train = X_train.reshape(len(train_set), *(28, 28, 1))
X_test = X_test.reshape(len(test_set), *(28, 28, 1))


y_train = training_array[:, 0]
y_test = testing_array[:,0]

y_train_cat = to_categorical(y_train, num_classes=25)
y_test_cat = to_categorical(y_test, num_classes=25)


# Here we create our labels for all alphabets excepts J and Z since their gesture involve a continous motion
labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y' ]


# Start loading our tensorFlow modules and define our batch size etc
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout

batch_size = 128
num_classes = 25
epochs = 10


# CNN Model
model = Sequential()
model.add(Conv2D(64, kernel_size=(3,3), activation='relu',input_shape= (28,28,1)))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.20))

model.add(Dense(num_classes,activation='softmax'))


# Compiling the model
model.compile(loss='categorical_crossentropy',optimizer=Adam(),metrics=['accuracy'])


print(model.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 26, 26, 64)        640       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 13, 13, 64)       0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 11, 11, 64)        36928     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 1, 1, 64)         0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 64)                0         
                                                                 
 dense (Dense)               (None, 128)               8320      
                                                                 
 dropout (Dropout)           (None, 128)               0         
                                                                 
 dense_1 (Dense)             (None, 25)                3225      
                                                                 
=================================================================
Total params: 86,041
Trainable params: 86,041
Non-trainable params: 0
_________________________________________________________________
None


# Model Training
model_train = model.fit(X_train,y_train_cat,validation_data=(X_test, y_test_cat),epochs=epochs,batch_size=batch_size)

Epoch 1/10
215/215 [==============================] - 34s 154ms/step - loss: 2.1894 - accuracy: 0.3249 - val_loss: 1.0104 - val_accuracy: 0.6630
Epoch 2/10
215/215 [==============================] - 31s 143ms/step - loss: 0.7063 - accuracy: 0.7616 - val_loss: 0.6540 - val_accuracy: 0.7790
Epoch 3/10
215/215 [==============================] - 36s 165ms/step - loss: 0.3595 - accuracy: 0.8785 - val_loss: 0.5327 - val_accuracy: 0.8140
Epoch 4/10
215/215 [==============================] - 41s 190ms/step - loss: 0.1919 - accuracy: 0.9384 - val_loss: 0.4934 - val_accuracy: 0.8473
Epoch 5/10
215/215 [==============================] - 41s 192ms/step - loss: 0.1051 - accuracy: 0.9697 - val_loss: 0.5082 - val_accuracy: 0.8642
Epoch 6/10
215/215 [==============================] - 35s 164ms/step - loss: 0.0703 - accuracy: 0.9796 - val_loss: 0.4841 - val_accuracy: 0.8783
Epoch 7/10
215/215 [==============================] - 40s 185ms/step - loss: 0.0463 - accuracy: 0.9878 - val_loss: 0.4552 - val_accuracy: 0.8943
Epoch 8/10
215/215 [==============================] - 39s 179ms/step - loss: 0.0326 - accuracy: 0.9916 - val_loss: 0.5102 - val_accuracy: 0.8799
Epoch 9/10
215/215 [==============================] - 37s 171ms/step - loss: 0.0233 - accuracy: 0.9943 - val_loss: 0.4858 - val_accuracy: 0.8834
Epoch 10/10
215/215 [==============================] - 37s 172ms/step - loss: 0.0219 - accuracy: 0.9939 - val_loss: 0.4685 - val_accuracy: 0.8907


# Saving the Model
model.save("Model1.h5")
print("Model Saved")

Model Saved


loss = model_train.history['loss']
val_loss = model_train.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'y', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()


# View our training history graphically
plt.plot(model_train.history['accuracy'])
plt.plot(model_train.history['val_accuracy'])
plt.title("Accuracy")
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['train','test'])

plt.show()


predictions=model.predict(X_test) 
classes=np.argmax(predictions,axis=1)


from sklearn.metrics import accuracy_score, f1_score
accuracy = accuracy_score(y_test, classes)
f1 = f1_score(y_test, classes,average='micro')
f1_macro = f1_score(y_test, classes,average='macro')
f1_wgt = f1_score(y_test, classes,average='weighted')
print('Accuracy Score = ', accuracy)
print('F1 Micro Score = ', f1)
print('F1 Macro Score = ', f1_macro)
print('F1 Weighted Score = ', f1_wgt)

Accuracy Score =  0.8906860011154489
F1 Micro Score =  0.8906860011154489
F1 Macro Score =  0.8814852827284719
F1 Weighted Score =  0.8903775368644098


print(classification_report(y_test, classes))

              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00       331
         1.0       1.00      0.91      0.95       432
         2.0       0.89      0.87      0.88       310
         3.0       1.00      0.84      0.92       245
         4.0       0.89      1.00      0.94       498
         5.0       1.00      1.00      1.00       247
         6.0       0.69      0.77      0.73       348
         7.0       0.99      0.95      0.97       436
         8.0       1.00      0.88      0.94       288
        10.0       0.91      0.87      0.89       331
        11.0       0.89      0.98      0.93       209
        12.0       0.92      0.88      0.90       394
        13.0       0.93      0.83      0.88       291
        14.0       0.89      0.76      0.82       246
        15.0       1.00      1.00      1.00       347
        16.0       0.81      1.00      0.89       164
        17.0       0.65      0.76      0.70       144
        18.0       0.82      0.85      0.84       246
        19.0       0.77      0.59      0.67       248
        20.0       0.90      0.92      0.91       266
        21.0       0.80      0.70      0.75       346
        22.0       0.71      1.00      0.83       206
        23.0       0.86      1.00      0.92       267
        24.0       0.90      0.94      0.92       332

    accuracy                           0.89      7172
   macro avg       0.88      0.89      0.88      7172
weighted avg       0.90      0.89      0.89      7172


# Tejas
model_2 = Sequential()

model_2.add(Conv2D(32, (3, 3), input_shape = (28,28,1), activation='relu'))
model_2.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(64, (3, 3), activation='relu'))
model_2.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(64, (3, 3), activation='relu'))
#model.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(128, (3, 3), activation='relu'))
#model.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Flatten())

model_2.add(Dense(128, activation = 'relu'))
model_2.add(Dense(25, activation = 'softmax'))


model_2.compile(loss ='categorical_crossentropy', optimizer='adam', metrics =['accuracy'])
model_2.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_3 (Conv2D)           (None, 26, 26, 32)        320       
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 13, 13, 32)       0         
 2D)                                                             
                                                                 
 dropout_1 (Dropout)         (None, 13, 13, 32)        0         
                                                                 
 conv2d_4 (Conv2D)           (None, 11, 11, 64)        18496     
                                                                 
 max_pooling2d_4 (MaxPooling  (None, 5, 5, 64)         0         
 2D)                                                             
                                                                 
 dropout_2 (Dropout)         (None, 5, 5, 64)          0         
                                                                 
 conv2d_5 (Conv2D)           (None, 3, 3, 64)          36928     
                                                                 
 dropout_3 (Dropout)         (None, 3, 3, 64)          0         
                                                                 
 conv2d_6 (Conv2D)           (None, 1, 1, 128)         73856     
                                                                 
 dropout_4 (Dropout)         (None, 1, 1, 128)         0         
                                                                 
 flatten_1 (Flatten)         (None, 128)               0         
                                                                 
 dense_2 (Dense)             (None, 128)               16512     
                                                                 
 dense_3 (Dense)             (None, 25)                3225      
                                                                 
=================================================================
Total params: 149,337
Trainable params: 149,337
Non-trainable params: 0
_________________________________________________________________


model_train_2 = model_2.fit(X_train,y_train_cat,validation_data=(X_test, y_test_cat),epochs=10,batch_size=256)

Epoch 1/10
108/108 [==============================] - 22s 195ms/step - loss: 2.9211 - accuracy: 0.1158 - val_loss: 2.1725 - val_accuracy: 0.3349
Epoch 2/10
108/108 [==============================] - 22s 201ms/step - loss: 1.6303 - accuracy: 0.4623 - val_loss: 1.0470 - val_accuracy: 0.6446
Epoch 3/10
108/108 [==============================] - 23s 207ms/step - loss: 0.8823 - accuracy: 0.6936 - val_loss: 0.5914 - val_accuracy: 0.7931
Epoch 4/10
108/108 [==============================] - 23s 216ms/step - loss: 0.5790 - accuracy: 0.7992 - val_loss: 0.4323 - val_accuracy: 0.8388
Epoch 5/10
108/108 [==============================] - 23s 216ms/step - loss: 0.4246 - accuracy: 0.8514 - val_loss: 0.3292 - val_accuracy: 0.8755
Epoch 6/10
108/108 [==============================] - 24s 218ms/step - loss: 0.3214 - accuracy: 0.8873 - val_loss: 0.2464 - val_accuracy: 0.9195
Epoch 7/10
108/108 [==============================] - 23s 216ms/step - loss: 0.2491 - accuracy: 0.9141 - val_loss: 0.2214 - val_accuracy: 0.9381
Epoch 8/10
108/108 [==============================] - 24s 218ms/step - loss: 0.1999 - accuracy: 0.9308 - val_loss: 0.1925 - val_accuracy: 0.9356
Epoch 9/10
108/108 [==============================] - 24s 224ms/step - loss: 0.1596 - accuracy: 0.9454 - val_loss: 0.1726 - val_accuracy: 0.9494
Epoch 10/10
108/108 [==============================] - 24s 220ms/step - loss: 0.1326 - accuracy: 0.9545 - val_loss: 0.1827 - val_accuracy: 0.9470


# Saving our Model
model_2.save("Model2.h5")
print("Model Saved")

Model Saved


loss = model_train_2.history['loss']
val_loss = model_train_2.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'y', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()


# View our training history graphically
plt.plot(model_train_2.history['accuracy'])
plt.plot(model_train_2.history['val_accuracy'])
plt.title("Accuracy")
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['train','test'])

plt.show()


predictions_2=model_2.predict(X_test) 
classes_2=np.argmax(predictions_2,axis=1)


accuracy = accuracy_score(y_test, classes_2)
f1 = f1_score(y_test, classes_2,average='micro')
f1_macro = f1_score(y_test, classes_2,average='macro')
f1_wgt = f1_score(y_test, classes_2,average='weighted')
print('Accuracy Score = ', accuracy)
print('F1 Micro Score = ', f1)
print('F1 Macro Score = ', f1_macro)
print('F1 Weighted Score = ', f1_wgt)

Accuracy Score =  0.947016174010039
F1 Micro Score =  0.947016174010039
F1 Macro Score =  0.9434506625628941
F1 Weighted Score =  0.9456151841405627


print(classification_report(y_test, classes_2))

              precision    recall  f1-score   support

         0.0       0.81      1.00      0.89       331
         1.0       1.00      1.00      1.00       432
         2.0       0.95      1.00      0.97       310
         3.0       1.00      0.91      0.96       245
         4.0       0.92      1.00      0.96       498
         5.0       1.00      1.00      1.00       247
         6.0       0.94      0.96      0.95       348
         7.0       0.91      0.95      0.93       436
         8.0       0.88      1.00      0.94       288
        10.0       0.94      0.97      0.96       331
        11.0       0.99      1.00      1.00       209
        12.0       0.97      0.92      0.94       394
        13.0       0.96      0.71      0.81       291
        14.0       1.00      1.00      1.00       246
        15.0       1.00      1.00      1.00       347
        16.0       0.91      1.00      0.95       164
        17.0       0.87      0.85      0.86       144
        18.0       0.97      0.90      0.93       246
        19.0       1.00      0.67      0.80       248
        20.0       0.96      0.90      0.93       266
        21.0       0.94      0.99      0.97       346
        22.0       1.00      1.00      1.00       206
        23.0       0.92      1.00      0.96       267
        24.0       1.00      0.88      0.94       332

    accuracy                           0.95      7172
   macro avg       0.95      0.94      0.94      7172
weighted avg       0.95      0.95      0.95      7172


# Create function to match label to letter
def getLetter(result):
    classLabels = { 0: 'A',
                    1: 'B',
                    2: 'C',
                    3: 'D',
                    4: 'E',
                    5: 'F',
                    6: 'G',
                    7: 'H',
                    8: 'I',
                    9: 'K',
                    10: 'L',
                    11: 'M',
                    12: 'N',
                    13: 'O',
                    14: 'P',
                    15: 'Q',
                    16: 'R',
                    17: 'S',
                    18: 'T',
                    19: 'U',
                    20: 'V',
                    21: 'W',
                    22: 'X',
                    23: 'Y'}
    try:
        res = int(result)
        return classLabels[res]
    except:
        return "Error"


# Model 2
# Starting the real time video capturing from webcam
cap = cv2.VideoCapture(0)

while True:

    ret, frame = cap.read()
    
    frame=cv2.flip(frame, 1)

    #ROI(Region of Interest)
    roi = frame[100:400, 320:620]

    #Displaying the ROI frame on webcam
    cv2.imshow('roi', roi)

    # Applying grayscale color format to the ROI
    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    roi = cv2.resize(roi, (28, 28), interpolation = cv2.INTER_AREA)

    
    cv2.imshow('roi sacled and gray', roi)
    copy = frame.copy()
    cv2.rectangle(copy, (320, 100), (620, 400), (255,0,0), 5)
    
    roi = roi.reshape(1,28,28,1) 

    # Prediction Based on the Hand Gesture
    result = model_2.predict(roi,1,verbose=0)[0]
    res = np.argmax(result)
    cv2.putText(copy, getLetter(str(res)), (300 , 100), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
    cv2.imshow('frame', copy)    
    
    #Enter Key
    if cv2.waitKey(1) == 13:
        break

# Stop The Webcam
cap.release()
cv2.destroyAllWindows()


# Function to create directory of images
def makedir(directory):
    if not os.path.exists(directory):
        os.makedirs(directory)
        return None
    else:
        pass


cap = cv2.VideoCapture(0)
i=0
image_count = 0
    
while i < 10:

    ret, frame = cap.read()
    frame = cv2.flip(frame, 1)

    #ROI(Region of Interest)
    roi = frame[100:400, 320:620]
    cv2.imshow('roi', roi)
    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    roi = cv2.resize(roi, (28, 28), interpolation = cv2.INTER_AREA)

    cv2.imshow('roi sacled and gray', roi)
    copy = frame.copy()
    cv2.rectangle(copy, (320, 100), (620, 400), (255,0,0), 5)
    
    if i == 0:
        image_count = 0
        cv2.putText(copy, "Hit Enter to Record when ready", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 1:
        image_count+=1
        cv2.putText(copy, "Recording 1st gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        # Training Images are Stored in this directory where each label has its respective gesture
        gesture_one = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/A/'
        makedir(gesture_one)
        cv2.imwrite(gesture_one + str(image_count) + ".jpg", roi)
    if i == 2:
        image_count+=1
        cv2.putText(copy, "Recording 1st gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        # Testing Images are Stored in this directory where each label has its respective gesture
        gesture_one = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/A/'
        makedir(gesture_one)
        cv2.imwrite(gesture_one + str(image_count) + ".jpg", roi)
    if i == 3:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 2nd gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 4:
        image_count+=1
        cv2.putText(copy, "Recording 2nd gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/B/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 5:
        image_count+=1
        cv2.putText(copy, "Recording 2nd gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/B/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==6:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 3rd gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==7:
        image_count+=1
        cv2.putText(copy, "Recording 3rd gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/C/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==8:
        image_count+=1
        cv2.putText(copy, "Recording 3rd gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/C/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 9:
        cv2.putText(copy, "Hit Enter to Exit", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    cv2.imshow('frame', copy)    

    #Enter Key
    if cv2.waitKey(1) == 13:
        image_count = 0
        i+=1

cap.release()
cv2.destroyAllWindows()


cap = cv2.VideoCapture(0)

i=0
image_count = 0
    
while i < 64:

    ret, frame = cap.read()
    frame = cv2.flip(frame, 1)

    #ROI(Region of Interest)
    roi = frame[100:400, 320:620]
    cv2.imshow('roi', roi)
    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    roi = cv2.resize(roi, (28, 28), interpolation = cv2.INTER_AREA)

    cv2.imshow('roi sacled and gray', roi)
    copy = frame.copy()
    cv2.rectangle(copy, (320, 100), (620, 400), (255,0,0), 5)
    
    if i == 0:
        image_count = 0
        cv2.putText(copy, "Hit Enter to Record when ready", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 1:
        image_count+=1
        cv2.putText(copy, "Recording 4th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_one = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/D/'
        makedir(gesture_one)
        cv2.imwrite(gesture_one + str(image_count) + ".jpg", roi)
    if i == 2:
        image_count+=1
        cv2.putText(copy, "Recording 4th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_one = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/D/'
        makedir(gesture_one)
        cv2.imwrite(gesture_one + str(image_count) + ".jpg", roi)
    if i == 3:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 5th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 4:
        image_count+=1
        cv2.putText(copy, "Recording 5th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/E/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 5:
        image_count+=1
        cv2.putText(copy, "Recording 5th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/E/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==6:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 6th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==7:
        image_count+=1
        cv2.putText(copy, "Recording 6th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/F/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==8:
        image_count+=1
        cv2.putText(copy, "Recording 6th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/F/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    if i == 9:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 7th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 10:
        image_count+=1
        cv2.putText(copy, "Recording 7th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/G/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 11:
        image_count+=1
        cv2.putText(copy, "Recording 7th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/G/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==12:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 8th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==13:
        image_count+=1
        cv2.putText(copy, "Recording 8th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/H/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==14:
        image_count+=1
        cv2.putText(copy, "Recording 8th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/H/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    if i == 15:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 9th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 16:
        image_count+=1
        cv2.putText(copy, "Recording 9th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/I/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 17:
        image_count+=1
        cv2.putText(copy, "Recording 9th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/I/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==18:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 10th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==19:
        image_count+=1
        cv2.putText(copy, "Recording 10th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/K/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==20:
        image_count+=1
        cv2.putText(copy, "Recording 10th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/K/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)




    if i == 21:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 11th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 22:
        image_count+=1
        cv2.putText(copy, "Recording 11th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/L/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 23:
        image_count+=1
        cv2.putText(copy, "Recording 11th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/L/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==24:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 12th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==25:
        image_count+=1
        cv2.putText(copy, "Recording 12th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/M/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==26:
        image_count+=1
        cv2.putText(copy, "Recording 12th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/M/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    

    if i == 27:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 13th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 28:
        image_count+=1
        cv2.putText(copy, "Recording 13th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/N/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 29:
        image_count+=1
        cv2.putText(copy, "Recording 13th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/N/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==30:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 14th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==31:
        image_count+=1
        cv2.putText(copy, "Recording 14th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/O/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==32:
        image_count+=1
        cv2.putText(copy, "Recording 14th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/O/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)





    if i == 33:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 15th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 34:
        image_count+=1
        cv2.putText(copy, "Recording 15th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/P/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 35:
        image_count+=1
        cv2.putText(copy, "Recording 15th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/P/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==36:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 16th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==37:
        image_count+=1
        cv2.putText(copy, "Recording 16th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/Q/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==38:
        image_count+=1
        cv2.putText(copy, "Recording 16th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/Q/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i == 39:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 17th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 40:
        image_count+=1
        cv2.putText(copy, "Recording 17th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/R/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 41:
        image_count+=1
        cv2.putText(copy, "Recording 17th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/R/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==42:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 18th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==43:
        image_count+=1
        cv2.putText(copy, "Recording 18th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/S/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==44:
        image_count+=1
        cv2.putText(copy, "Recording 18th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/S/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    if i == 45:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 19th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 46:
        image_count+=1
        cv2.putText(copy, "Recording 19th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/T/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 47:
        image_count+=1
        cv2.putText(copy, "Recording 19th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/T/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==48:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 20th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==49:
        image_count+=1
        cv2.putText(copy, "Recording 20th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/U/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==50:
        image_count+=1
        cv2.putText(copy, "Recording 20th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/U/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    if i == 51:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 21st gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 52:
        image_count+=1
        cv2.putText(copy, "Recording 21st gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/V/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 53:
        image_count+=1
        cv2.putText(copy, "Recording 21st gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/V/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==54:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 22nd gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==55:
        image_count+=1
        cv2.putText(copy, "Recording 22nd gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/W/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==56:
        image_count+=1
        cv2.putText(copy, "Recording 22nd gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/W/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)



    if i == 57:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 23rd gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    if i == 58:
        image_count+=1
        cv2.putText(copy, "Recording 23rd gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/X/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i == 59:
        image_count+=1
        cv2.putText(copy, "Recording 23rd gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/X/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i==60:
        cv2.putText(copy, "Hit Enter to Record when ready to Record 24th gesture", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)

    if i==61:
        image_count+=1
        cv2.putText(copy, "Recording 24th gesture - Train", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train/Y/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)
    if i==62:
        image_count+=1
        cv2.putText(copy, "Recording 24th gesture - Test", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        cv2.putText(copy, str(image_count), (400 , 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
        gesture_two = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test/Y/'
        makedir(gesture_two)
        cv2.imwrite(gesture_two + str(image_count) + ".jpg", roi)

    if i == 63:
        cv2.putText(copy, "Hit Enter to Exit", (100 , 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)
    cv2.imshow('frame', copy)    

    #Enter Key
    if cv2.waitKey(1) == 13: 
        image_count = 0
        i+=1

cap.release()
cv2.destroyAllWindows()


num_classes = 24
img_rows, img_cols = 28, 28
batch_size = 120

# Defining directories for training and testing images
train_data_dir = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/train'
validation_data_dir = 'C:/Users/Sushant/Desktop/Applied Machine Learning/Assignments/Project/handgestures/test'


# Applying different transformations on the training images
train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=30,
      width_shift_range=0.3,
      height_shift_range=0.3,
      horizontal_flip=True,
      fill_mode='nearest')
 
validation_datagen = ImageDataGenerator(rescale=1./255)
 
 # Converting the training images to grayscale
train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        color_mode = 'grayscale',
        class_mode='categorical')
 
  # Converting the testing images to grayscale
validation_generator = validation_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        color_mode = 'grayscale',
        class_mode='categorical')

Found 50760 images belonging to 24 classes.
Found 22744 images belonging to 24 classes.


#Model 3
model_3 = Sequential()
model_3.add(Conv2D(64, kernel_size=(3,3), activation = 'relu', input_shape=(28,28,1) ))
model_3.add(MaxPooling2D(pool_size = (2, 2)))

model_3.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu'))
model_3.add(MaxPooling2D(pool_size = (2, 2)))

model_3.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu'))
model_3.add(MaxPooling2D(pool_size = (2, 2)))

model_3.add(Flatten())
model_3.add(Dense(128, activation = 'relu'))
model_3.add(Dropout(0.20))

model_3.add(Dense(24, activation = 'softmax'))

print(model.summary())

Model: "sequential_16"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_50 (Conv2D)          (None, 26, 26, 64)        640       
                                                                 
 max_pooling2d_46 (MaxPoolin  (None, 13, 13, 64)       0         
 g2D)                                                            
                                                                 
 conv2d_51 (Conv2D)          (None, 11, 11, 64)        36928     
                                                                 
 max_pooling2d_47 (MaxPoolin  (None, 5, 5, 64)         0         
 g2D)                                                            
                                                                 
 conv2d_52 (Conv2D)          (None, 3, 3, 64)          36928     
                                                                 
 max_pooling2d_48 (MaxPoolin  (None, 1, 1, 64)         0         
 g2D)                                                            
                                                                 
 flatten_16 (Flatten)        (None, 64)                0         
                                                                 
 dense_32 (Dense)            (None, 128)               8320      
                                                                 
 dropout_22 (Dropout)        (None, 128)               0         
                                                                 
 dense_33 (Dense)            (None, 1)                 129       
                                                                 
=================================================================
Total params: 82,945
Trainable params: 82,945
Non-trainable params: 0
_________________________________________________________________
None


model_3.compile(loss = 'categorical_crossentropy',
              optimizer = 'rmsprop',
              metrics = ['accuracy'])

nb_train_samples = 50760
nb_validation_samples = 22744 
epochs = 50

history = model_3.fit(
    train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs,
    validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)

Epoch 1/50
423/423 [==============================] - 107s 249ms/step - loss: 3.0635 - accuracy: 0.0828 - val_loss: 2.7215 - val_accuracy: 0.2590
Epoch 2/50
423/423 [==============================] - 115s 272ms/step - loss: 2.8043 - accuracy: 0.1529 - val_loss: 2.1315 - val_accuracy: 0.3199
Epoch 3/50
423/423 [==============================] - 105s 248ms/step - loss: 2.5859 - accuracy: 0.2112 - val_loss: 1.6250 - val_accuracy: 0.4452
Epoch 4/50
423/423 [==============================] - 109s 257ms/step - loss: 2.4237 - accuracy: 0.2609 - val_loss: 1.3979 - val_accuracy: 0.4976
Epoch 5/50
423/423 [==============================] - 110s 259ms/step - loss: 2.2555 - accuracy: 0.3078 - val_loss: 1.1101 - val_accuracy: 0.6901
Epoch 6/50
423/423 [==============================] - 105s 248ms/step - loss: 2.1064 - accuracy: 0.3506 - val_loss: 1.0409 - val_accuracy: 0.6840
Epoch 7/50
423/423 [==============================] - 111s 262ms/step - loss: 1.9877 - accuracy: 0.3814 - val_loss: 0.7556 - val_accuracy: 0.7415
Epoch 8/50
423/423 [==============================] - 103s 244ms/step - loss: 1.8761 - accuracy: 0.4157 - val_loss: 1.1790 - val_accuracy: 0.5979
Epoch 9/50
423/423 [==============================] - 106s 251ms/step - loss: 1.7962 - accuracy: 0.4394 - val_loss: 0.6082 - val_accuracy: 0.7761
Epoch 10/50
423/423 [==============================] - 108s 256ms/step - loss: 1.7168 - accuracy: 0.4603 - val_loss: 0.8326 - val_accuracy: 0.7472
Epoch 11/50
423/423 [==============================] - 86s 204ms/step - loss: 1.6485 - accuracy: 0.4772 - val_loss: 0.5019 - val_accuracy: 0.8235
Epoch 12/50
423/423 [==============================] - 88s 209ms/step - loss: 1.5842 - accuracy: 0.4991 - val_loss: 0.4810 - val_accuracy: 0.8165
Epoch 13/50
423/423 [==============================] - 82s 194ms/step - loss: 1.5360 - accuracy: 0.5168 - val_loss: 0.3467 - val_accuracy: 0.8844
Epoch 14/50
423/423 [==============================] - 86s 203ms/step - loss: 1.4934 - accuracy: 0.5268 - val_loss: 0.4833 - val_accuracy: 0.8200
Epoch 15/50
423/423 [==============================] - 84s 198ms/step - loss: 1.4366 - accuracy: 0.5435 - val_loss: 0.5031 - val_accuracy: 0.8430
Epoch 16/50
423/423 [==============================] - 84s 198ms/step - loss: 1.3979 - accuracy: 0.5554 - val_loss: 0.3526 - val_accuracy: 0.8961
Epoch 17/50
423/423 [==============================] - 86s 203ms/step - loss: 1.3641 - accuracy: 0.5663 - val_loss: 0.3547 - val_accuracy: 0.8877
Epoch 18/50
423/423 [==============================] - 79s 188ms/step - loss: 1.3310 - accuracy: 0.5733 - val_loss: 0.4263 - val_accuracy: 0.8524
Epoch 19/50
423/423 [==============================] - 79s 185ms/step - loss: 1.2984 - accuracy: 0.5852 - val_loss: 0.2928 - val_accuracy: 0.9129
Epoch 20/50
423/423 [==============================] - 79s 187ms/step - loss: 1.2763 - accuracy: 0.5920 - val_loss: 0.2474 - val_accuracy: 0.9274
Epoch 21/50
423/423 [==============================] - 79s 186ms/step - loss: 1.2397 - accuracy: 0.6010 - val_loss: 0.2912 - val_accuracy: 0.9250
Epoch 22/50
423/423 [==============================] - 80s 188ms/step - loss: 1.2212 - accuracy: 0.6070 - val_loss: 0.4049 - val_accuracy: 0.8636
Epoch 23/50
423/423 [==============================] - 78s 185ms/step - loss: 1.2034 - accuracy: 0.6144 - val_loss: 0.1939 - val_accuracy: 0.9522
Epoch 24/50
423/423 [==============================] - 80s 189ms/step - loss: 1.1767 - accuracy: 0.6241 - val_loss: 0.1894 - val_accuracy: 0.9387
Epoch 25/50
423/423 [==============================] - 3481s 8s/step - loss: 1.1627 - accuracy: 0.6286 - val_loss: 0.2000 - val_accuracy: 0.9311
Epoch 26/50
423/423 [==============================] - 114s 270ms/step - loss: 1.1362 - accuracy: 0.6336 - val_loss: 0.2049 - val_accuracy: 0.9454
Epoch 27/50
423/423 [==============================] - 96s 228ms/step - loss: 1.1146 - accuracy: 0.6400 - val_loss: 0.2080 - val_accuracy: 0.9308
Epoch 28/50
423/423 [==============================] - 111s 261ms/step - loss: 1.0988 - accuracy: 0.6449 - val_loss: 0.2386 - val_accuracy: 0.9136
Epoch 29/50
423/423 [==============================] - 102s 241ms/step - loss: 1.0903 - accuracy: 0.6467 - val_loss: 0.0927 - val_accuracy: 0.9786
Epoch 30/50
423/423 [==============================] - 105s 247ms/step - loss: 1.0677 - accuracy: 0.6561 - val_loss: 0.0966 - val_accuracy: 0.9769
Epoch 31/50
423/423 [==============================] - 108s 256ms/step - loss: 1.0628 - accuracy: 0.6586 - val_loss: 0.1117 - val_accuracy: 0.9747
Epoch 32/50
423/423 [==============================] - 102s 242ms/step - loss: 1.0404 - accuracy: 0.6639 - val_loss: 0.1343 - val_accuracy: 0.9619
Epoch 33/50
423/423 [==============================] - 107s 253ms/step - loss: 1.0450 - accuracy: 0.6611 - val_loss: 0.1488 - val_accuracy: 0.9563
Epoch 34/50
423/423 [==============================] - 99s 234ms/step - loss: 1.0262 - accuracy: 0.6697 - val_loss: 0.1845 - val_accuracy: 0.9484
Epoch 35/50
423/423 [==============================] - 87s 205ms/step - loss: 1.0138 - accuracy: 0.6727 - val_loss: 0.1158 - val_accuracy: 0.9737
Epoch 36/50
423/423 [==============================] - 80s 188ms/step - loss: 0.9961 - accuracy: 0.6776 - val_loss: 0.2601 - val_accuracy: 0.9276
Epoch 37/50
423/423 [==============================] - 87s 206ms/step - loss: 0.9969 - accuracy: 0.6766 - val_loss: 0.1406 - val_accuracy: 0.9663
Epoch 38/50
423/423 [==============================] - 79s 187ms/step - loss: 0.9811 - accuracy: 0.6830 - val_loss: 0.0846 - val_accuracy: 0.9785
Epoch 39/50
423/423 [==============================] - 86s 204ms/step - loss: 0.9750 - accuracy: 0.6841 - val_loss: 0.1294 - val_accuracy: 0.9653
Epoch 40/50
423/423 [==============================] - 80s 189ms/step - loss: 0.9654 - accuracy: 0.6871 - val_loss: 0.1097 - val_accuracy: 0.9786
Epoch 41/50
423/423 [==============================] - 84s 199ms/step - loss: 0.9452 - accuracy: 0.6935 - val_loss: 0.1413 - val_accuracy: 0.9523
Epoch 42/50
423/423 [==============================] - 83s 196ms/step - loss: 0.9402 - accuracy: 0.6944 - val_loss: 0.1076 - val_accuracy: 0.9681
Epoch 43/50
423/423 [==============================] - 84s 199ms/step - loss: 0.9384 - accuracy: 0.6981 - val_loss: 0.1288 - val_accuracy: 0.9597
Epoch 44/50
423/423 [==============================] - 84s 198ms/step - loss: 0.9314 - accuracy: 0.6983 - val_loss: 0.0717 - val_accuracy: 0.9851
Epoch 45/50
423/423 [==============================] - 81s 192ms/step - loss: 0.9274 - accuracy: 0.6981 - val_loss: 0.1521 - val_accuracy: 0.9447
Epoch 46/50
423/423 [==============================] - 85s 202ms/step - loss: 0.9180 - accuracy: 0.7014 - val_loss: 0.1037 - val_accuracy: 0.9643
Epoch 47/50
423/423 [==============================] - 81s 190ms/step - loss: 0.9120 - accuracy: 0.7053 - val_loss: 0.0660 - val_accuracy: 0.9831
Epoch 48/50
423/423 [==============================] - 88s 207ms/step - loss: 0.9008 - accuracy: 0.7077 - val_loss: 0.2924 - val_accuracy: 0.9123
Epoch 49/50
423/423 [==============================] - 8040s 19s/step - loss: 0.9016 - accuracy: 0.7082 - val_loss: 0.0972 - val_accuracy: 0.9734
Epoch 50/50
423/423 [==============================] - 119s 282ms/step - loss: 0.8954 - accuracy: 0.7117 - val_loss: 0.1324 - val_accuracy: 0.9531


# Model_2
model_2 = Sequential()

model_2.add(Conv2D(32, (3, 3), input_shape = (28,28,1), activation='relu'))
model_2.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(64, (3, 3), activation='relu'))
model_2.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(64, (3, 3), activation='relu'))
#model.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Conv2D(128, (3, 3), activation='relu'))
#model.add(MaxPooling2D(pool_size = (2, 2)))
model_2.add(Dropout(0.3))

model_2.add(Flatten())

model_2.add(Dense(128, activation = 'relu'))
model_2.add(Dense(24, activation = 'softmax'))


# Model_2
model_2.compile(loss = 'categorical_crossentropy',
              optimizer = 'rmsprop',
              metrics = ['accuracy'])

nb_train_samples = 50760
nb_validation_samples = 22744 
epochs = 50

history = model_2.fit(
    train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs,
    validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)

Epoch 1/50
24/24 [==============================] - 11s 421ms/step - loss: 1.1010 - accuracy: 0.3247 - val_loss: 1.0965 - val_accuracy: 0.3427
Epoch 2/50
24/24 [==============================] - 9s 392ms/step - loss: 1.0978 - accuracy: 0.3507 - val_loss: 1.0843 - val_accuracy: 0.6778
Epoch 3/50
24/24 [==============================] - 9s 389ms/step - loss: 1.0929 - accuracy: 0.3742 - val_loss: 1.0397 - val_accuracy: 0.6644
Epoch 4/50
24/24 [==============================] - 9s 395ms/step - loss: 1.0886 - accuracy: 0.3875 - val_loss: 1.0128 - val_accuracy: 0.6631
Epoch 5/50
24/24 [==============================] - 10s 388ms/step - loss: 1.0797 - accuracy: 0.4161 - val_loss: 0.9570 - val_accuracy: 0.6698
Epoch 6/50
24/24 [==============================] - 9s 386ms/step - loss: 1.0728 - accuracy: 0.4192 - val_loss: 0.9622 - val_accuracy: 0.6480
Epoch 7/50
24/24 [==============================] - 11s 455ms/step - loss: 1.0616 - accuracy: 0.4338 - val_loss: 0.9010 - val_accuracy: 0.6547
Epoch 8/50
24/24 [==============================] - 11s 462ms/step - loss: 1.0505 - accuracy: 0.4526 - val_loss: 0.8809 - val_accuracy: 0.6631
Epoch 9/50
24/24 [==============================] - 11s 461ms/step - loss: 1.0294 - accuracy: 0.4656 - val_loss: 0.7961 - val_accuracy: 0.6840
Epoch 10/50
24/24 [==============================] - 11s 444ms/step - loss: 1.0099 - accuracy: 0.4928 - val_loss: 0.7145 - val_accuracy: 0.7213
Epoch 11/50
24/24 [==============================] - 11s 456ms/step - loss: 1.0112 - accuracy: 0.4974 - val_loss: 0.6781 - val_accuracy: 0.7147
Epoch 12/50
24/24 [==============================] - 11s 438ms/step - loss: 0.9757 - accuracy: 0.5151 - val_loss: 0.6857 - val_accuracy: 0.8729
Epoch 13/50
24/24 [==============================] - 11s 438ms/step - loss: 0.9738 - accuracy: 0.5163 - val_loss: 0.6407 - val_accuracy: 0.8000
Epoch 14/50
24/24 [==============================] - 11s 440ms/step - loss: 0.9585 - accuracy: 0.5361 - val_loss: 0.6499 - val_accuracy: 0.7769
Epoch 15/50
24/24 [==============================] - 9s 371ms/step - loss: 0.9410 - accuracy: 0.5286 - val_loss: 0.6410 - val_accuracy: 0.8973
Epoch 16/50
24/24 [==============================] - 9s 382ms/step - loss: 0.9128 - accuracy: 0.5680 - val_loss: 0.5119 - val_accuracy: 0.7151
Epoch 17/50
24/24 [==============================] - 9s 376ms/step - loss: 0.9324 - accuracy: 0.5644 - val_loss: 0.6264 - val_accuracy: 0.7227
Epoch 18/50
24/24 [==============================] - 11s 436ms/step - loss: 0.8939 - accuracy: 0.5738 - val_loss: 0.5046 - val_accuracy: 0.9271
Epoch 19/50
24/24 [==============================] - 10s 428ms/step - loss: 0.8872 - accuracy: 0.5668 - val_loss: 0.5706 - val_accuracy: 0.8111
Epoch 20/50
24/24 [==============================] - 10s 431ms/step - loss: 0.8888 - accuracy: 0.5745 - val_loss: 0.5523 - val_accuracy: 0.6676
Epoch 21/50
24/24 [==============================] - 10s 431ms/step - loss: 0.9064 - accuracy: 0.5716 - val_loss: 0.5453 - val_accuracy: 0.6849
Epoch 22/50
24/24 [==============================] - 10s 429ms/step - loss: 0.8356 - accuracy: 0.6210 - val_loss: 0.4492 - val_accuracy: 0.7164
Epoch 23/50
24/24 [==============================] - 11s 436ms/step - loss: 0.8856 - accuracy: 0.5772 - val_loss: 0.4561 - val_accuracy: 0.6987
Epoch 24/50
24/24 [==============================] - 11s 449ms/step - loss: 0.8291 - accuracy: 0.6065 - val_loss: 0.5311 - val_accuracy: 0.8449
Epoch 25/50
24/24 [==============================] - 11s 460ms/step - loss: 0.8342 - accuracy: 0.6053 - val_loss: 0.4574 - val_accuracy: 0.6676
Epoch 26/50
24/24 [==============================] - 10s 421ms/step - loss: 0.8253 - accuracy: 0.6089 - val_loss: 0.4055 - val_accuracy: 0.7676
Epoch 27/50
24/24 [==============================] - 9s 370ms/step - loss: 0.8440 - accuracy: 0.6085 - val_loss: 0.3982 - val_accuracy: 0.9640
Epoch 28/50
24/24 [==============================] - 9s 376ms/step - loss: 0.8352 - accuracy: 0.6171 - val_loss: 0.3810 - val_accuracy: 0.8969
Epoch 29/50
24/24 [==============================] - 9s 374ms/step - loss: 0.7934 - accuracy: 0.6250 - val_loss: 0.3737 - val_accuracy: 0.9338
Epoch 30/50
24/24 [==============================] - 11s 429ms/step - loss: 0.8085 - accuracy: 0.6268 - val_loss: 0.4821 - val_accuracy: 0.8018
Epoch 31/50
24/24 [==============================] - 11s 447ms/step - loss: 0.7937 - accuracy: 0.6347 - val_loss: 0.4344 - val_accuracy: 0.8120
Epoch 32/50
24/24 [==============================] - 11s 463ms/step - loss: 0.7693 - accuracy: 0.6498 - val_loss: 0.4643 - val_accuracy: 0.6778
Epoch 33/50
24/24 [==============================] - 13s 518ms/step - loss: 0.7672 - accuracy: 0.6558 - val_loss: 0.3556 - val_accuracy: 0.8511
Epoch 34/50
24/24 [==============================] - 12s 516ms/step - loss: 0.7436 - accuracy: 0.6510 - val_loss: 0.3470 - val_accuracy: 0.9644
Epoch 35/50
24/24 [==============================] - 13s 519ms/step - loss: 0.7623 - accuracy: 0.6398 - val_loss: 0.5727 - val_accuracy: 0.6671
Epoch 36/50
24/24 [==============================] - 13s 522ms/step - loss: 0.7432 - accuracy: 0.6438 - val_loss: 0.3531 - val_accuracy: 0.9716
Epoch 37/50
24/24 [==============================] - 13s 516ms/step - loss: 0.7201 - accuracy: 0.6701 - val_loss: 0.4569 - val_accuracy: 0.6702
Epoch 38/50
24/24 [==============================] - 13s 517ms/step - loss: 0.7393 - accuracy: 0.6520 - val_loss: 0.3433 - val_accuracy: 0.8244
Epoch 39/50
24/24 [==============================] - 13s 526ms/step - loss: 0.7295 - accuracy: 0.6523 - val_loss: 0.3961 - val_accuracy: 0.7036
Epoch 40/50
24/24 [==============================] - 12s 483ms/step - loss: 0.7181 - accuracy: 0.6575 - val_loss: 0.3454 - val_accuracy: 0.7831
Epoch 41/50
24/24 [==============================] - 11s 447ms/step - loss: 0.7078 - accuracy: 0.6679 - val_loss: 0.3477 - val_accuracy: 0.7627
Epoch 42/50
24/24 [==============================] - 9s 375ms/step - loss: 0.7089 - accuracy: 0.6595 - val_loss: 0.3414 - val_accuracy: 0.9520
Epoch 43/50
24/24 [==============================] - 9s 378ms/step - loss: 0.7364 - accuracy: 0.6481 - val_loss: 0.4091 - val_accuracy: 0.6827
Epoch 44/50
24/24 [==============================] - 9s 376ms/step - loss: 0.7009 - accuracy: 0.6666 - val_loss: 0.3673 - val_accuracy: 0.7671
Epoch 45/50
24/24 [==============================] - 9s 397ms/step - loss: 0.6839 - accuracy: 0.6830 - val_loss: 0.3211 - val_accuracy: 0.9244
Epoch 46/50
24/24 [==============================] - 10s 426ms/step - loss: 0.6851 - accuracy: 0.6717 - val_loss: 0.3231 - val_accuracy: 0.9591
Epoch 47/50
24/24 [==============================] - 10s 434ms/step - loss: 0.6837 - accuracy: 0.6876 - val_loss: 0.3087 - val_accuracy: 0.9987
Epoch 48/50
24/24 [==============================] - 10s 434ms/step - loss: 0.6504 - accuracy: 0.6943 - val_loss: 0.5607 - val_accuracy: 0.6769
Epoch 49/50
24/24 [==============================] - 11s 433ms/step - loss: 0.6633 - accuracy: 0.6859 - val_loss: 0.3268 - val_accuracy: 0.8244
Epoch 50/50
24/24 [==============================] - 10s 422ms/step - loss: 0.6461 - accuracy: 0.7056 - val_loss: 0.6402 - val_accuracy: 0.6636


# Saving our Model
model_3.save("Model_3.h5")


model_2.save("Model_2.h5")


# Starting the real time video capturing from webcam
cap = cv2.VideoCapture(0)

while True:

    ret, frame = cap.read()
    
    frame=cv2.flip(frame, 1)

    #ROI(Region of Interest)
    roi = frame[100:400, 320:620]

    #Displaying the ROI frame on webcam
    cv2.imshow('roi', roi)

    # Applying grayscale color format to the ROI
    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    roi = cv2.resize(roi, (28, 28), interpolation = cv2.INTER_AREA)
    
    cv2.imshow('roi scaled and gray', roi)
    copy = frame.copy()
    cv2.rectangle(copy, (320, 100), (620, 400), (255,0,0), 5)

    #cv2.putText(copy, "Place your hand in the box", (300 , 100), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)

    # Prediction Based on the Hand Gesture
    roi = roi.reshape(1,28,28,1) 
    roi = roi/255
    result = model_2.predict(roi,1)[0]
    res = np.argmax(result)
    print(result)
  
    cv2.putText(copy, getLetter(res) , (300 , 100), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
    cv2.imshow('frame', copy)    
    
    #Enter Key
    if cv2.waitKey(1) == 13:
        break

# Stop The Webcam      
cap.release()
cv2.destroyAllWindows()

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 3.4990754e-07 7.1574703e-02 1.2793539e-07 4.7793137e-03]
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[4.52565067e-02 2.48326346e-01 1.53250721e-06 1.34054535e-05
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 4.0001519e-02 3.3610612e-12 3.3902287e-04 2.4101057e-23 2.5830823e-05
 1.8971372e-12 1.4355633e-01 5.8773585e-17 1.3023209e-05]
[7.43380561e-02 4.02659148e-01 2.83208390e-08 5.70515022e-06
 3.82690163e-15 1.32340638e-09 3.26638150e-26 4.77449885e-11
 1.32334226e-18 1.07158990e-17 1.16440857e-15 6.49087679e-06
 2.25004598e-11 2.05189796e-10 3.95779542e-09 1.74402878e-01
 1.04692616e-10 8.96125101e-03 1.68540867e-19 4.67556471e-04
 2.29457304e-11 3.38315576e-01 1.12342380e-13 8.43239715e-04]
[1.6446908e-01 7.3001331e-01 2.4281333e-10 2.3521063e-06 8.3291473e-12
 1.5808846e-10 2.1375493e-22 7.6773234e-13 1.1211532e-15 8.0318237e-18
 4.1479890e-15 3.2054373e-07 3.8708767e-10 2.8064090e-10 1.5274271e-08
 1.4772630e-02 5.2314099e-12 3.2023687e-04 4.5532108e-21 1.8366793e-05
 7.4026453e-12 9.0389647e-02 1.8098898e-16 1.3940561e-05]
[6.4441544e-01 3.5529402e-01 3.0320828e-07 2.2026447e-06 8.8442488e-05
 5.6726387e-11 6.5052262e-11 9.9871673e-12 4.9747905e-05 5.1291362e-14
 5.6363425e-09 6.3329082e-08 4.2832303e-06 1.1182214e-08 4.8629616e-05
 3.5388555e-05 2.8364550e-10 4.8202838e-07 9.4004442e-14 2.8463577e-07
 1.9923037e-09 6.0579787e-05 4.4500871e-14 5.7135637e-08]
[2.2375929e-01 7.7428180e-01 1.2727024e-07 5.4739558e-06 1.6065828e-04
 9.0235625e-10 3.7431658e-10 3.4010736e-10 3.1000833e-05 1.4239422e-12
 1.1433148e-08 1.5465789e-06 1.3160322e-05 4.2039613e-08 6.5463082e-06
 1.7655129e-04 1.9727400e-09 7.0534356e-06 4.3650177e-13 1.1655708e-05
 6.1708745e-09 1.5425725e-03 8.1662894e-13 2.6021739e-06]
[1.9697741e-01 7.9209286e-01 7.3403356e-08 2.9039207e-05 6.7612703e-04
 5.5037077e-09 2.4388935e-10 1.4972641e-09 9.3060080e-05 2.9235798e-11
 3.5434191e-08 1.4362842e-05 9.7047574e-05 8.7011166e-08 5.8399987e-06
 5.4246327e-04 9.0481223e-09 4.7429101e-05 1.6848266e-12 9.4173389e-05
 3.3417749e-08 9.3030492e-03 4.5627274e-12 2.6932694e-05]
[1.9069861e-01 8.0052930e-01 6.4401206e-08 2.2920480e-05 6.8076851e-04
 3.9080215e-09 2.4484978e-10 1.0966751e-09 1.0775648e-04 2.8314351e-11
 3.7822705e-08 1.2175947e-05 9.9377525e-05 6.1250553e-08 4.1254762e-06
 3.0383866e-04 1.0168238e-08 3.3815722e-05 1.7561665e-12 7.2632414e-05
 3.7050562e-08 7.4141501e-03 5.1217663e-12 2.0255398e-05]
[1.51927903e-01 5.56381464e-01 1.24618333e-08 1.17906646e-04
 6.48445785e-02 2.66714961e-09 4.34692637e-09 1.98731920e-09
 1.69720098e-01 2.32444863e-09 3.38823229e-05 5.13941886e-05
 4.97375578e-02 4.89353020e-08 8.94230652e-06 2.44596049e-05
 2.98817758e-05 1.67111120e-05 1.09249143e-09 1.01136771e-04
 7.86404926e-05 6.92233071e-03 5.81093085e-09 3.01995919e-06]
[1.6995364e-01 6.6828614e-01 2.6030123e-09 3.1437122e-04 4.8082266e-02
 7.2308022e-09 3.2196256e-11 2.2048476e-11 3.4344107e-02 1.0306350e-08
 2.8688389e-06 1.3936967e-04 6.7186214e-02 2.1197009e-08 1.2679410e-06
 1.6175485e-05 4.5423949e-06 3.5316698e-05 2.5922578e-11 9.5564828e-05
 1.4964872e-05 1.1512900e-02 3.6040099e-10 1.0337373e-05]
[2.7054790e-01 7.1495289e-01 2.6127575e-10 2.6938249e-04 5.0297123e-03
 3.0915397e-09 2.9206011e-14 4.7528869e-15 5.5409764e-04 3.7094796e-09
 1.0739429e-08 4.2952084e-05 5.1136226e-03 1.6086092e-09 7.2195824e-08
 2.9075572e-06 1.1905684e-08 1.3843215e-05 8.3624132e-15 8.9658197e-06
 9.2805479e-08 3.4563993e-03 2.3888788e-13 7.1998975e-06]
[4.6026298e-01 5.3066581e-01 2.2434094e-08 2.1158349e-04 1.9674138e-03
 6.9353514e-09 2.9431589e-11 4.3362189e-12 7.7773887e-04 6.2707300e-10
 6.8693190e-08 5.3858781e-05 3.4804982e-03 2.5524113e-08 1.5353382e-05
 2.7180737e-04 1.6792207e-08 4.0382474e-05 2.1579880e-12 2.8698014e-05
 7.4435022e-08 2.2166602e-03 5.1869056e-12 7.0543401e-06]
[3.4612080e-01 6.5260327e-01 7.3553350e-09 2.1277070e-05 2.1224012e-05
 6.3378025e-10 1.7085453e-13 2.6127643e-13 6.7358167e-07 1.1341455e-12
 1.9614957e-10 2.2919266e-06 2.2243677e-05 6.9831914e-09 2.0807431e-05
 3.8296936e-04 4.6731737e-11 1.2911941e-05 9.0688324e-16 2.3446585e-06
 1.3048645e-10 7.8819104e-04 9.3347698e-15 1.0335568e-06]
[2.3590711e-01 7.3500687e-01 1.9305656e-08 2.7392478e-05 2.3488914e-05
 6.0379910e-09 1.9572819e-13 3.5778994e-11 1.8540219e-06 3.1998742e-12
 9.6934083e-10 3.8116890e-05 3.2404377e-04 7.9393516e-08 1.1901718e-05
 1.8321853e-02 3.9938763e-09 1.3544421e-04 2.3841926e-13 4.4721106e-05
 5.2326463e-09 1.0143515e-02 3.5553125e-12 1.3500053e-05]
[2.3777452e-01 7.3515880e-01 1.8594077e-08 2.8131501e-05 2.5218165e-05
 5.7532041e-09 2.6304688e-13 4.2666648e-11 1.7091337e-06 3.1647690e-12
 1.0005449e-09 4.4271248e-05 3.4468257e-04 8.0219657e-08 1.0369471e-05
 1.6852966e-02 3.1660237e-09 1.5638236e-04 2.2927959e-13 4.6554633e-05
 3.9329793e-09 9.5421337e-03 3.5575943e-12 1.4188560e-05]
[2.3647365e-01 7.1967417e-01 1.4652395e-08 3.4755925e-05 1.5845035e-05
 8.1823810e-09 6.8648544e-14 4.7259387e-11 6.5884046e-07 2.8799120e-12
 5.9144228e-10 5.5527842e-05 2.7782860e-04 8.4648072e-08 6.7463593e-06
 2.6626561e-02 3.6096210e-09 2.5575858e-04 1.4039151e-13 7.6653632e-05
 4.6857598e-09 1.6477386e-02 3.7392298e-12 2.4373430e-05]
[2.4703407e-01 6.7346239e-01 1.0072296e-08 5.3102944e-05 1.3262783e-06
 1.4956468e-08 1.7434799e-15 3.4057580e-11 1.0108130e-08 9.6884447e-13
 8.3725450e-11 7.0663307e-05 3.5878173e-05 5.4348629e-08 1.6029583e-06
 3.9643422e-02 1.3944305e-09 7.6016667e-04 8.1985378e-15 1.4800856e-04
 1.6995929e-09 3.8702339e-02 1.3713545e-12 8.6819760e-05]
[1.03066534e-01 5.11526227e-01 5.02981514e-08 5.85265570e-06
 1.86911820e-13 2.36080955e-09 1.35343098e-23 2.47385914e-11
 8.70527153e-16 1.93770923e-16 7.35236626e-15 8.18607259e-06
 3.86190663e-10 2.29381958e-10 2.83744495e-08 1.91936150e-01
 1.12894062e-10 2.70559010e-03 6.39517527e-18 3.51303519e-04
 5.64210657e-11 1.89785793e-01 2.37622747e-13 6.14234013e-04]
[5.6580290e-02 9.2029178e-01 1.3100640e-10 5.4110053e-08 4.6410192e-18
 1.2130574e-12 9.8399432e-30 1.6468300e-15 1.4188284e-22 3.5644176e-22
 2.6014287e-19 2.2767608e-09 2.2026853e-15 3.8731290e-13 2.7148911e-10
 1.7802530e-03 6.0126078e-14 2.1901036e-05 9.4745227e-26 8.2280610e-07
 7.0068287e-14 2.1323558e-02 3.8049331e-19 1.3371800e-06]
[6.5105617e-02 9.1847754e-01 1.6215241e-09 5.3950668e-08 2.7964336e-18
 2.2850802e-13 1.2840204e-29 4.2005774e-15 3.5026262e-22 1.5328423e-22
 1.8982045e-19 1.6334442e-09 2.2567594e-15 4.0057467e-13 6.6658790e-09
 6.9768145e-03 3.0319416e-14 1.7758777e-05 7.9381495e-25 8.6915247e-07
 3.2310943e-14 9.4204471e-03 4.8616791e-19 7.8134144e-07]
[7.0637271e-02 9.1780448e-01 2.2997147e-08 5.8505027e-08 7.9508663e-19
 5.5349722e-12 3.4961255e-28 2.1633619e-13 1.6308307e-21 6.2972569e-21
 1.0197675e-17 1.2237710e-08 7.2313566e-15 1.8393240e-12 3.4281191e-09
 5.5749044e-03 1.1768156e-11 2.8510316e-05 2.1254713e-21 1.7333980e-06
 4.6751067e-12 5.9502940e-03 1.2139746e-15 2.7475885e-06]
[1.5217136e-01 8.0822957e-01 2.5609996e-08 2.4825857e-07 1.2503195e-18
 6.5444983e-11 1.3277466e-28 6.2925994e-13 1.6881501e-21 2.3183074e-20
 3.0279069e-17 3.8614619e-08 2.2458350e-14 6.0545379e-12 4.0274788e-09
 1.5995637e-02 4.2855892e-11 1.5698973e-04 2.9830958e-21 5.9036297e-06
 2.3747216e-11 2.3410706e-02 2.8253815e-15 2.9464825e-05]

	label	pixel1	pixel2	pixel3	pixel4	pixel5	pixel6	pixel7	pixel8	pixel9	...	pixel775	pixel776	pixel777	pixel778	pixel779	pixel780	pixel781	pixel782	pixel783	pixel784
count	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	...	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000	27455.000000
mean	12.318813	145.419377	148.500273	151.247714	153.546531	156.210891	158.411255	160.472154	162.339683	163.954799	...	141.104863	147.495611	153.325806	159.125332	161.969259	162.736696	162.906137	161.966454	161.137898	159.824731
std	7.287552	41.358555	39.942152	39.056286	38.595247	37.111165	36.125579	35.016392	33.661998	32.651607	...	63.751194	65.512894	64.427412	63.708507	63.738316	63.444008	63.509210	63.298721	63.610415	64.396846
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	6.000000	121.000000	126.000000	130.000000	133.000000	137.000000	140.000000	142.000000	144.000000	146.000000	...	92.000000	96.000000	103.000000	112.000000	120.000000	125.000000	128.000000	128.000000	128.000000	125.500000
50%	13.000000	150.000000	153.000000	156.000000	158.000000	160.000000	162.000000	164.000000	165.000000	166.000000	...	144.000000	162.000000	172.000000	180.000000	183.000000	184.000000	184.000000	182.000000	182.000000	182.000000
75%	19.000000	174.000000	176.000000	178.000000	179.000000	181.000000	182.000000	183.000000	184.000000	185.000000	...	196.000000	202.000000	205.000000	207.000000	208.000000	207.000000	207.000000	206.000000	204.000000	204.000000
max	24.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	...	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000

AML Project: American Sign Language Recogntition using Deep Learning¶

This can be said to be a balanced dataset. The number of labels are in the range 900 to slightly above 1200¶

Plotting the learning curves for our model¶

Here we saw that this model overfits on the data. The reason is because of the high number of neurons used in the CNN model. So now we add a dropout layer after each Pooling layer with a dropout rate 0f 30%¶

Learning Curves for the 2nd Model¶

From the learning curves, accuracy scores and classification report we see that this model fits very well on the data and we get a good accuracy for the model. So now we test this model on the live input from webcam¶

Testing on live input¶

Here we create our dataset for every hand gesture. We are creating training and testing data here itself. Since we are generating the images we can specify any number of images for hand gestures that we want. Thus there will be no problem of class imbalance here.¶

After augmentation, a total of 50760 images were created having the specified styling and transformation for training the model and a total of 22744 images were created for validation set¶

Model Training on the Augmented Dataset¶

Now that the model has been trained on the real data we test our model¶

	label	pixel1	pixel2	pixel3	pixel4	pixel5	pixel6	pixel7	pixel8	pixel9	...	pixel775	pixel776	pixel777	pixel778	pixel779	pixel780	pixel781	pixel782	pixel783	pixel784
0	3	107	118	127	134	139	143	146	150	153	...	207	207	207	207	206	206	206	204	203	202
1	6	155	157	156	156	156	157	156	158	158	...	69	149	128	87	94	163	175	103	135	149
2	2	187	188	188	187	187	186	187	188	187	...	202	201	200	199	198	199	198	195	194	195
3	2	211	211	212	212	211	210	211	210	210	...	235	234	233	231	230	226	225	222	229	163
4	13	164	167	170	172	176	179	180	184	185	...	92	105	105	108	133	163	157	163	164	179

	label	pixel1	pixel2	pixel3	pixel4	pixel5	pixel6	pixel7	pixel8	pixel9	...	pixel775	pixel776	pixel777	pixel778	pixel779	pixel780	pixel781	pixel782	pixel783	pixel784
0	3	107	118	127	134	139	143	146	150	153	...	207	207	207	207	206	206	206	204	203	202
1	6	155	157	156	156	156	157	156	158	158	...	69	149	128	87	94	163	175	103	135	149
2	2	187	188	188	187	187	186	187	188	187	...	202	201	200	199	198	199	198	195	194	195
3	2	211	211	212	212	211	210	211	210	210	...	235	234	233	231	230	226	225	222	229	163
4	13	164	167	170	172	176	179	180	184	185	...	92	105	105	108	133	163	157	163	164	179

	label	pixel1	pixel2	pixel3	pixel4	pixel5	pixel6	pixel7	pixel8	pixel9	...	pixel775	pixel776	pixel777	pixel778	pixel779	pixel780	pixel781	pixel782	pixel783	pixel784
0	3	107	118	127	134	139	143	146	150	153	...	207	207	207	207	206	206	206	204	203	202
1	6	155	157	156	156	156	157	156	158	158	...	69	149	128	87	94	163	175	103	135	149
2	2	187	188	188	187	187	186	187	188	187	...	202	201	200	199	198	199	198	195	194	195
3	2	211	211	212	212	211	210	211	210	210	...	235	234	233	231	230	226	225	222	229	163
4	13	164	167	170	172	176	179	180	184	185	...	92	105	105	108	133	163	157	163	164	179